Science in Lübeck:Focus on Life

Research

Speech comprehension with a cochlear implant

Some cochlear implant recipients learn to understand speech exceedingly well while others struggle with speech comprehension difficulties. A novel hearing test shortly after implantation can accurately predict future speech recognition.Currently the only neuroprostheses that can successfully...

Currently the only neuroprostheses that can successfully replace a sensory organ are cochlear implants: They can restore hearing in deaf patients. Cochlear implants pick up sound and translate it into an electric signal which is transmitted directly to the auditory nerve, thus by-passing the damaged middle or inner ear.

The transduced sound signal is considerably distorted, however, sounding like a “harsh whisper”, as some patients would describe it. Cochlear implant recipients vary largely in how well they adapt to their device: Some learn to comprehend speech even under difficult listening condi¬tions, such as on the phone or in a pub, while others hardly benefit from their device. The source of this variability is still elusive.

Researchers at the Max Planck Institute for Human Cognitive and Brain Sciences, the Cochlear Implant Center Leipzig, and the University of Lübeck tested newly implanted adults on their capacity to hear temporal modulations in noise. This novel hearing test turned out to be highly correlated with future speech recognition: Patients, who could better hear temporal modulations were also the ones, who had a better speech comprehension six months later. In contrast, the ones with poor auditory temporal resolution had an increased risk of poor future speech comprehension.

For the growing number of cochlear implant patients (to date approximately 150 000 in Europe), this adaptation process is a vital necessity. After a period of deafness, adjusting to the extremely degraded, unfamiliar signal delivered by the implant enables the listener to extract meaning from a messy auditory signal. Novel, reliable hearing tests are essential with respect to two points: On the one hand, they allow to identify poor performers early on and to consequently prescribe appropriate training measures. On the other hand, they can help to improve CI algorithms on an individual basis.